3 of the 21 OOS orders were by MSA
They made 2 “OOS Regional MSA” orders 1 “Regional Counselor MSAs” order.
General observations
Of 121,110 students purchased between 2017-2020, 52,788 reside in California. About 44% of student purchased (*assuming students weren’t purchased more than once).
Of 52,788 students purchased in California, 19,071 were purchased in the LA metro area alone (36%).
After merging CEEB and NCES data, there were 45,172 students purchased in California, 18,417 had zip codes that did not match the zip code for the high school they attended. Perhaps wehave zip codes of the home addresses of students purchased?
All “non-purchased” zipcodes included below are zip codes that make up the LA metro area that were not purchased.
The zip code from the student lists was used here. In other words, zip code represents the zip code associated with students’ address.
acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=median_household_income, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean median household income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_white_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_asian_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_hispanic_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_black_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") Large variation in the number of students purchased by zip codes (student-level zip code)
acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=median_household_income, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_white_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_asian_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_hispanic_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_black_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") The table below sorts the number of students purchased by zip code in descending order for LA msa.
acs_zip_pur_laThe table below displays median income and race/ethicity characteristics of zip codes that were not purchased in the LA msa area.
acs_zip_npur_laSimilar to LA msa, I am using the zip code associated with the student list zip code (students’ address).
All “non-purchased” zipcodes included below are zip codes in the NY metro area where a student was not purchased.
The zip code from the student lists data set was used here. In other words, zip code represents the zip code associated with students’ address.
acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=median_household_income, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_white_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity",fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_asian_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity",fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_hispanic_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity",fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_black_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") Following the same logic and plotting number of students purchased by zip code.
Large variation in the number of students purchased by zip codes (student-level zip code)
acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=median_household_income, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_white_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_asian_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_hispanic_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_black_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") The table below sorts the number of students purchased by zip code in descending order.
acs_zip_pur_nyThe table below displays median income and race/ethnicity characteristics of zip codes that were not purchased in the NY metro area.
acs_zip_npur_nySimilar to LA & NY msa, I am using the zip code associated with the student list zip code (students’ address).
All “non-purchased” zipcodes included below are zip codes in the Austin metro area where a student was not purchased.
The zip code from the student lists was used here. In other words, zip code represents the zip code associated with students’ address.
acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=median_household_income, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_white_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity",fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_asian_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_hispanic_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_black_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") Following the same logic and plotting number of students purchased by their zip codes.
Large variation in the number of students purchased by zip codes (student-level zip code)
acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=median_household_income, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_white_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity",fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_asian_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_hispanic_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_black_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") The table below sorts in descending order the number of students purchased by zip code.
acs_zip_pur_austin The table below displays median income and race/ethnicity characteristics of zipcodes that were not purchased in the Austin metro area.
acs_zip_npur_austinSimilar to LA msa, I am using the zip code associated with the student list zip code (students’ address).
All “non-purchased” zipcodes included below are zip codes in the California where a student was not purchased.
The zip code from the student lists was used here. In other words, zip code represents the zip code associated with students’ address.
acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=median_household_income, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_white_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_asian_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_hispanic_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_grouped %>%
ggplot(aes(x=as.factor(zip_purchased), y=pop_black_15_19_pct, color=zip_purchased)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Non-purchased vs. Purchased zip codes",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") Following the same logic and plotting number of students purchased by their zip codes.
Large variation in the number of students purchased by zip codes (student-level zip code)
acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=median_household_income, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean median income") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_white_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent White") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_asian_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Asian") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_hispanic_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Hispanic") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") acs_race_zipcode_groupedv2 %>%
ggplot(aes(x=as.factor(purchased_num_cat), y=pop_black_15_19_pct, color=purchased_num_cat)) +
geom_bar(stat="identity", fill="white") +
labs(x = "Number of students purchased by zip code",
y = "Mean percent Black") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), legend.position = "none") The table below sorts in descending order the number of students purchased by zip code.
acs_zip_pur_caThe table below displays median income and race/ethnicity characteristics of zipcodes that were not purchased in California.
acs_zip_npur_ca